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Tinne Tuytelaars
Researcher at Katholieke Universiteit Leuven
Publications - 413
Citations - 52215
Tinne Tuytelaars is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Object detection & Computer science. The author has an hindex of 71, co-authored 374 publications receiving 46089 citations. Previous affiliations of Tinne Tuytelaars include Idiap Research Institute & IMEC.
Papers
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Book ChapterDOI
SURF: speeded up robust features
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Journal ArticleDOI
Speeded-Up Robust Features (SURF)
TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Journal ArticleDOI
A Comparison of Affine Region Detectors
Krystian Mikolajczyk,Tinne Tuytelaars,Cordelia Schmid,Andrew Zisserman,Jiri Matas,Frederik Schaffalitzky,Timor Kadir,L. Van Gool +7 more
TL;DR: A snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions to establish a reference test set of images and performance software so that future detectors can be evaluated in the same framework.
Proceedings ArticleDOI
Unsupervised Visual Domain Adaptation Using Subspace Alignment
TL;DR: This paper introduces a new domain adaptation algorithm where the source and target domains are represented by subspaces described by eigenvectors, and seeks a domain adaptation solution by learning a mapping function which aligns the source subspace with the target one.
Book
Local Invariant Feature Detectors: A Survey
TL;DR: An overview of invariant interest point detectors can be found in this paper, where an overview of the literature over the past four decades organized in different categories of feature extraction methods is presented.